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The present status of assisted reproductive technology in Asia-Oceania

Chang YS, Jun JK, Choi YM, Moon SY

Asia Oceania J Obstet Gynaecol, 1994 Dec;20(4):331-44.
PMID: 7832663

This is a survey on the present status of assisted reproductive technology in the Asia-Oceanic region. The survey formats were sent to the 19-member societies of AOFOG. By the end of August 1991, 11 countries responded: Australia, Egypt, Hong Kong, Israel, Japan, Malaysia, New Zealand, Philippines, Republic of China, Singapore and Thailand. This report is a summary of data from 12 countries including Korea. It comprised of 14 headings: IVF, GIFT, AIH, AID, donor sperm in ART, donor eggs in ART, preembryos from IVF for donation, cryopreservation of eggs, cryopreservation of fertilized eggs and preembryos, research of preembryos, surrogate mothers, additional procedures, quality assurance in reproductive technology and formation of policy for emerging reproductive technology. Each heading is composed of status of regulations, cost and coverage and the results and management of program.
Fulltext Use of traditional, complementary and alternative medicine in nine countries: A cross-sectional multinational survey

Youn BY, Moon S, Mok K, Cheon C, Ko Y, Park S, et al.

Complement Ther Med, 2022 Dec;71:102889.
PMID: 36162719 DOI: 10.1016/j.ctim.2022.102889

OBJECTIVES: Traditional, complementary, and alternative medicine (TC&AM) play an exceptional role in health care around the world as many patients has sought a holistic approach.
SETTING: In this study, a multinational survey was developed and administered to obtain experience, attitude, and promotion information with regard to the international use of TC&AM among nine countries: Germany, United States, Japan, China, Malaysia, Vietnam, Russia, Kazakhstan, and United Arab Emirates (UAE). The survey was administered via online to members of SurveyMonkey Audience, a proprietary panel of respondents who were recruited from a diverse population worldwide.
RESULTS: A total of 1071 participants has completed the survey. The participants were in favor of the treatments and therapies as well as expressed positive attitudes and also have used herbal medicine treatment more than acupuncture therapy and also used the modalities to promote metabolism rather than treating musculoskeletal diseases. Moreover, participants mentioned that TC&AM should be applied for treating and managing infectious diseases, such as COVID-19. Additionally, participants recommended using Facebook channel to promote its treatments and therapies.
CONCLUSION: Based on the results, this study provides initial insights on TC&AM that may influence the non-users globally and perhaps inspire a need for further research including more countries in different continents.
Prediction of Mortality Among Patients With Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multi-Center Cohort Study

Song J, Shin SD, Jamaluddin SF, Chiang WC, Tanaka H, Song KJ, et al.

J Neurotrauma, 2023 Jul;40(13-14):1376-1387.
PMID: 36656672 DOI: 10.1089/neu.2022.0280

Abstract Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.
Building a tuberculosis-free world: The Lancet Commission on tuberculosis

Reid MJA, Arinaminpathy N, Bloom A, Bloom BR, Boehme C, Chaisson R, et al.

Lancet, 2019 Mar 30;393(10178):1331-1384.
PMID: 30904263 DOI: 10.1016/S0140-6736(19)30024-8